Selecting the forecast interval of wind generations

2015 ◽  
Vol 11 (1) ◽  
pp. 126-128 ◽  
Author(s):  
Wei Wei ◽  
Feng Liu ◽  
Shengwei Mei
Keyword(s):  
Forecast Interval

scholarly journals Uncertainty‐aware forecast interval for hourly PV power output

2019 ◽  
Vol 13 (14) ◽  
pp. 2656-2664 ◽  
Author(s):  
HyunYong Lee ◽  
Nac‐Woo Kim ◽  
Jun‐Gi Lee ◽  
Byung‐Tak Lee
Keyword(s):  
Power Output ◽  
Forecast Interval

scholarly journals Ending Storm Version of the 7-day Weighted Analog Intensity Prediction Technique for Western North Pacific Tropical Cyclones

2017 ◽  
Vol 32 (6) ◽  
pp. 2229-2235 ◽  
Author(s):  
Hsiao-Chung Tsai ◽  
Russell L. Elsberry
Keyword(s):  
Tropical Cyclones ◽  
North Pacific ◽  
Western North Pacific ◽  
Storm Events ◽  
Training Set ◽  
Intensity Prediction ◽  
Prediction Technique ◽  
The Mean ◽  
Forecast Interval ◽  
Intensity Spread

Abstract The weighted analog intensity prediction technique for western North Pacific (WAIP) tropical cyclones (TCs) was the first guidance product for 7-day intensity forecasts, which is skillful in the sense that the 7-day errors are about the same as the 5-day errors. Independent tests of this WAIP version revealed an increasingly large intensity overforecast bias as the forecast interval was extended from 5 to 7 days, which was associated with “ending storms” due to landfall, extratropical transition, or to delayed development. Thus, the 7-day WAIP has been modified to separately forecast ending and nonending storms within the 7-day forecast interval. The additional ending storm constraint in the selection of the 10 best historical analogs is that the intensity at the last matching point with the target TC track cannot exceed 50 kt (where 1 kt = 0.51 m s−1). A separate intensity bias correction calculated for the ending storm training set reduces the mean biases to near-zero values and thereby improves the mean absolute errors in the 5–7-day forecast interval for the independent set. A separate calibration of the intensity spreads for the training set to ensure that 68% of the verifying intensities will be within the 12-h WAIP intensity spread values results in smaller spreads (or higher confidence) for ending storms in the 5–7-day forecast intervals. Thus, some extra effort by the forecasters to identify ending storm events within 7 days will allow improved intensity and intensity spread forecast guidance.

scholarly journals Development and Analysis of a Probabilistic Forecasting Game for Meteorology Students

2012 ◽  
Vol 93 (12) ◽  
pp. 1833-1843
Author(s):  
Steven G. Decker
Keyword(s):  
Undergraduate Students ◽  
Operating Characteristic ◽  
Weather Forecasting ◽  
Student Feedback ◽  
Brier Score ◽  
Probabilistic Forecasting ◽  
Relative Operating Characteristic ◽  
Rutgers University ◽  
Forecast Interval ◽  
Temperature Errors

Calls for moving from a deterministic to a probabilistic view of weather forecasting have become increasingly urgent over recent decades, yet the primary national forecasting competition and many in-class forecasting games are wholly deterministic in nature. To counter these conflicting trends, a long-running forecasting game at Rutgers University has recently been modified to become probabilistic in nature. Students forecast high- and low-temperature intervals and probabilities of precipitation for two locations: one fixed at the Rutgers cooperative observing station, the other chosen for each forecast window to maximize difficulty. Precipitation errors are tabulated with a Brier score, while temperature errors contain a sharpness component dependent on the width of the forecast interval and an interval miss component dependent on the degree to which the verification falls within the interval. The inclusion of a probabilistic forecasting game allows for the creation of a substantial database of forecasts that can be analyzed using standard probabilistic approaches, such as reliability diagrams, relative operating characteristic curves, and histograms. Discussions of probabilistic forecast quality can be quite abstract for undergraduate students, but the use of a forecast database that students themselves help construct motivates these discussions and helps students make connections between their forecast process, their standing in class rankings, and the verification diagrams they use. Student feedback on the probabilistic game is also discussed.

scholarly journals THE DETERMINANTS OF MANAGEMENT FORECASTS ERROR AND THE IPO UNDERPRICING: A CASE STUDY OF INDONESIAN IPO

2011 ◽  
Vol 3 (2) ◽  
pp. 136-152
Author(s):  
Yanthi Hutagaol ◽  
Florens Siauw ◽  
Irwan A. Ekaputra
Keyword(s):  
Information Asymmetry ◽  
Stock Exchange ◽  
Market Condition ◽  
Forecast Errors ◽  
Management Forecasts ◽  
Ipo Underpricing ◽  
Earning Forecast ◽  
Expected Earnings ◽  
Forecast Interval

To reduce the well-known information asymmetry in the IPO market, the issuing firms are required to publish offering prospectuses. One type of information disclosed in the prospectus is the management financial forecasts in which the IPO firms predict expected earnings at the end of year after the listing. The purpose of this study is to investigate the determinants of forecasted error published by the management in the IPO prospectuses. This study observes six possible determinants that affect the absolute forecast errors (AFE). Furthermore, this study also examines whether the earning forecast errors could explain the IPO stylish underpricing phenomenon.A sample of 124 IPO firms that went public in Indonesian Stock Exchange (prior Jakarta Stock Exchange) during the 1997 – 2005 period. The results show that the research models proposed are valid models. The management AFE is determined by firm size, forecast interval period, industry, and the firm business range.  This study also finds that the AFE is positively related to the IPO underpricing, suggesting that the higher the forecast errors, the more underpriced is the IPO. Moreover, it is also found that market condition also influences the underpricing level in Indonesian IPO market.

scholarly journals Seven-Day Intensity and Intensity Spread Predictions for Atlantic Tropical Cyclones

2017 ◽  
Vol 32 (1) ◽  
pp. 141-147 ◽  
Author(s):  
Hsiao-Chung Tsai ◽  
Russell L. Elsberry
Keyword(s):  
Tropical Cyclones ◽  
Independent Set ◽  
Correlation Coefficients ◽  
Training Set ◽  
Prediction Technique ◽  
The Mean ◽  
Forecast Interval ◽  
Intensity Spread

Abstract The extension of the Weighted Analog Intensity Atlantic (WAIA) prediction technique for Atlantic tropical cyclones (TCs) from 5 to 7 days revealed a need for two modifications. The first modification for the 7-day WAIA was to randomly select 70% of the TCs in the entire 2000–15 sample to be the training set and use the remaining 30% as the independent set. The second modification was to ensure that appropriate analogs were selected for ending storm situations such as landfall, postrecurvature, and nondevelopment or delayed intensification within the 7-day forecast interval. By simply constraining the analog selection such that the intensity at the last matching point with the target TC track does not exceed 50 kt (where 1 kt = 0.51 m s−1), an increasing overforecast bias with forecast interval was almost eliminated in both the training set and the independent set. With these two analog selection modifications, the mean absolute errors, and the correlation coefficients of the 7-day WAIA intensities with the verifying intensities, are essentially constant from 5 to 7 days, which establishes this WAIA as a viable technique for 7-day intensity forecasts of Atlantic TCs.

scholarly journals Predictability Associated with the Downstream Impacts of the Extratropical Transition of Tropical Cyclones: Methodology and a Case Study of Typhoon Nabi (2005)

2008 ◽  
Vol 136 (9) ◽  
pp. 3205-3225 ◽  
Author(s):  
Patrick A. Harr ◽  
Doris Anwender ◽  
Sarah C. Jones
Keyword(s):  
North Pacific ◽  
Function Analysis ◽  
Potential Temperature ◽  
Fuzzy Cluster ◽  
Temporal Organization ◽  
Ensemble Prediction ◽  
Extratropical Transition ◽  
Ensemble Prediction System ◽  
Forecast Interval ◽  
Standard Deviations

Abstract Measures of the variability among ensemble members from the National Centers for Environmental Prediction ensemble prediction system are examined with respect to forecasts of the extratropical transition (ET) of Typhoon Nabi over the western North Pacific during September 2005. In this study, variability among ensemble members is used as a proxy for predictability. The time–longitude distribution of standard deviations among 500-hPa height fields from the ensemble members is found to increase across the North Pacific following the completion of the extratropical transition. Furthermore, the increase in ensemble standard deviation is organized such that an increase is associated with the extratropical transition and another increase extends downstream from the ET event. The organization and amplitude of the standard deviations increase from 144 h until approximately 72–48 h prior to the completion of the extratropical transition, and then decrease as the forecast interval decreases. An empirical orthogonal function analysis of potential temperature on the dynamic tropopause is applied to ensemble members to identify the spatial and temporal organization of centers of variability related to the extratropical transition. The principal components are then used in a fuzzy cluster analysis to examine the grouping of forecast sequences in the collection of ensemble members. The number of forecast groups decreases as the forecast interval to the completion of the ET decreases. However, there is a systematic progression of centers of variability downstream of the ET event. Once the variability associated with the ET begins to decrease, the variability downstream of the ET event also begins to decrease.

scholarly journals Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 577 ◽  
Author(s):  
Jane ◽  
Parker ◽  
Vaucher ◽  
Berman
Keyword(s):  
Energy Storage ◽  
Fuel Consumption ◽  
Weather Forecast ◽  
Atmospheric Conditions ◽  
Diesel Generator ◽  
Energy Management Strategy ◽  
Hybrid Microgrid ◽  
Electrical Grids ◽  
Forecast Interval ◽  
Electrical Generation

A microgrid consists of electrical generation sources, energy storage assets, loads, and the ability to function independently, or connect and share power with other electrical grids. Thefocus of this work is on the behavior of a microgrid, with both diesel generator and photovoltaic resources, whose heating or cooling loads are influenced by local meteorological conditions. Themicrogrid's fuel consumption and energy storage requirement were then examined as a function of the atmospheric conditions used by its energy management strategy (EMS). A fuel-optimal EMS, able to exploit meteorological forecasts, was developed and evaluated using a hybrid microgrid simulation. Weather forecast update periods ranged from 15 min to 24 h. Four representative meteorological sky classifications (clear, partly cloudy, overcast, or monsoon) were considered. Forall four sky classifications, fuel consumption and energy storage requirements increased linearly with the increasing weather forecast interval. Larger forecast intervals lead to degraded weather forecasts, requiring more frequent charging/discharging of the energy storage, increasing both the fuel consumption and energy storage design requirements. The significant contributions of this work include the optimal EMS and an approach for quantifying the meteorological forecast effects on fuel consumption and energy storage requirements on microgrid performance. The findings of this study indicate that the forecast interval used by the EMS affected both fuel consumption and energy storage requirements, and that the sensitivity of these effects depended on the 24-hour sky conditions.

scholarly journals Ultra-short-term Load Forecasting Based on Real-Time Response of Classified Flexible Loads

2020 ◽  
Vol 165 ◽  
pp. 03048
Author(s):  
Dunnan Liu ◽  
Pengfei Li ◽  
Xiaofeng Xu ◽  
Yiding Jin ◽  
Shanzhe Shi ◽  
...  
Keyword(s):  
Demand Response ◽  
Load Forecasting ◽  
Short Term ◽  
Total Load ◽  
Term Forecast ◽  
Short Term Load Forecasting ◽  
Flexible Loads ◽  
Flexible Load ◽  
Forecast Interval ◽  
Important Basis

Ultra-short-term load forecasting is an important basis for optimization and adjustment of power generation plans and dispatch plans. Based on the radial basis function neural network, the inert load is predicted, and the flexible load is predicted based on the price elasticity of electricity demand. Then, combined with the range of the flexible load, an ultra-short-term forecast interval for the total load is constructed. This paper studies the total load after considering the flexible load for demand response, and verifies the feasibility of the proposed method with an example.

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